Optimal control for two wheeled self-balancing robot based on butterfly optimization algorithm

This paper examined the two-wheeled self-balancing robot mathematical model. A Linear Quadratic Regulator (LQR) controller was then successfully used by the author for this system. The trial and error method and two optimization algorithms, particle swarm optimization (PSO) and butterfly optimization algorithm (BOA), are suggested for tuning the LQR controller parameters. The comparison between the three LQR controller tuning techniques is performed to select the best one. With the help of the Python program, the performance of the control strategy is investigated and shown with regard to the tilt and heading angles. Controlling outcomes were tested through a simulation, and it was proved that the LQR control system based on the BOA succeeded in finding a better response in tracking tilt and heading angles with enhancement percentages of 61.111% and 78.348% than the LQR response based on PSO and the trial and error techniques, respectively.